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Data and methods

Data, and the methods used to analyze them, are the foundation for evidence-based research. Articles in this subject area discuss the value of different types of data collection, and explain important statistical and econometric methods that provide ways to summarize and present information, and to identify and quantify correlation or causality.

Summary measures of inequality differ from one
another and give different pictures of the evolution of economic inequality
over time

Economists use various metrics for measuring
income inequality. Here, the most commonly used measures—the Lorenz curve,
the Gini coefficient, decile ratios, the Palma ratio, and the Theil
index—are discussed in relation to their benefits and limitations. Equally
important is the choice of what to measure: pre-tax and after-tax income,
consumption, and wealth are useful indicators; and different sources of
income such as wages, capital gains, taxes, and benefits can be examined.
Understanding the dimensions of economic inequality is a key first step
toward choosing the right policies to address it.

Measuring work hours correctly is important,
but different surveys can tell different stories

Work hours are key components in estimating
productivity growth and hourly wages as well as being a useful cyclical
indicator in their own right, so measuring them correctly is important. The
US Bureau of Labor Statistics (BLS) collects data on work hours in several
surveys and publishes four widely used series that measure average weekly
hours. The series tell different stories about average weekly hours and
trends in those hours but qualitatively similar stories about the cyclical
behavior of work hours. The research summarized here explains the
differences in levels, but only some of the differences in trends.

Incentivized measures are considered to be the
gold standard in measuring individuals’ risk preferences, but is that
correct?

Risk aversion is an important factor in many
settings, including individual decisions about investment or occupational
choice, and government choices about policies affecting environmental,
industrial, or health risks. Risk preferences are measured using surveys or
incentivized games with real consequences. Reviewing the different
approaches to measuring individual risk aversion shows that the best
approach will depend on the question being asked and the study's target
population. In particular, economists’ gold standard of incentivized games
may not be superior to surveys in all settings.

Measures of intergenerational persistence can be
indicative of equality of opportunity, but the relationship is not
clear-cut

A strong association between incomes across
generations—with children from poor families likely to be poor as adults—is
frequently considered an indicator of insufficient equality of opportunity.
Studies of such “intergenerational persistence,” or lack of
intergenerational mobility, measure the strength of the relationship between
parents’ socio-economic status and that of their children as adults.
However, the association between equality of opportunity and common measures
of intergenerational persistence is not as clear-cut as is often assumed. To
aid interpretation researchers often compare measures across time and space
but must recognize that reliable measurement requires overcoming important
data and methodological difficulties.

Studies of independent contractors suggest that
workers’ effort may be more responsive to wage incentives than previously
thought

A fundamental question in economic policy is how
labor supply responds to changes in remuneration. The responsiveness of
labor supply determines the size of the employment impact and efficiency
loss of progressive income taxation. It also affects predictions about the
impacts of policies ranging from fiscal responses to business cycles to
government transfer programs. The characteristics of jobs held by
independent contractors provide an opportunity to overcome problems faced by
earlier studies and help answer this fundamental question.

New sources of data create challenges that may require new skills

Big Data refers to data sets of much larger size, higher frequency, and often more personalized information. Examples include data collected by smart sensors in homes or aggregation of tweets on Twitter. In small data sets, traditional econometric methods tend to outperform more complex techniques. In large data sets, however, machine learning methods shine. New analytic approaches are needed to make the most of Big Data in economics. Researchers and policymakers should thus pay close attention to recent developments in machine learning techniques if they want to fully take advantage of these new sources of Big Data.

Should statistical criteria for measuring employment and
unemployment be re-examined?

Measuring employment and unemployment is essential for economic
policy. Internationally agreed measures (e.g. headcount employment and unemployment rates
based on standard definitions) enhance comparability across time and space, but changes in
real labor markets and policy agendas challenge these traditional conventions. Boundaries
between different labor market states are blurred, complicating identification. Individual
experiences in each state may vary considerably, highlighting the importance of how each
employed or unemployed person is weighted in statistical indices.

More important than defining and measuring
informality is focusing on reducing its detrimental consequences

There are more informal workers than formal
workers across the globe, and yet there remains confusion as to what makes
workers or firms informal and how to measure the extent of it. Informal work
and informal economic activities imply large efficiency and welfare losses,
in terms of low productivity, low earnings, sub-standard working conditions,
and lack of social insurance coverage. Rather than quibbling over
definitions and measures of informality, it is crucial for policymakers to
address these correlates of informality in order to mitigate the negative
efficiency and welfare effects.

Are experiments the gold standard or just
over-hyped?

Non-experimental evaluations of programs compare
individuals who choose to participate in a program to individuals who do
not. Such comparisons run the risk of conflating non-random selection into
the program with its causal effects. By randomly assigning individuals to
participate in the program or not, experimental evaluations remove the
potential for non-random selection to bias comparisons of participants and
non-participants. In so doing, they provide compelling causal evidence of
program effects. At the same time, experiments are not a panacea, and
require careful design and interpretation.

Knowing the real cost of children is important
for crafting better economic policy

The cost of children is a critical parameter
used in determining many economic policies. For instance, correctly setting
the tax deduction for families with children requires assessing the true
household cost of children. Evaluating child poverty at the individual level
requires making a clear distinction between the share of family resources
received by children and that received by parents. The standard ad hoc
measures (equivalence scales) used in official publications to measure the
cost of children are arbitrary and are not informed by any economic theory.
However, economists have developed methods that are grounded in economic
theory and can replace ad hoc measures.